




"""simple docstring"""




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class        __magic_name__				:






				'''simple docstring'''





				def __init__(			self						,					_a						,					_a=sys.maxsize					):





							"""simple docstring"""
							lowerCamelCase      =   """bilinear"""
							lowerCamelCase      =   max_size
							lowerCamelCase      =   short_edge_length





				def __call__(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   []
							for img in imgs:
										lowerCamelCase ,  lowerCamelCase      =   img.shape[:2]
										# later: provide list and randomly choose index for resize
										lowerCamelCase      =   np.random.randint(self.short_edge_length[0]						,					self.short_edge_length[1] + 1					)
										if size == 0:
													return img
										lowerCamelCase      =   size * 1.0 / min(_a						,					_a					)
										if h < w:
													lowerCamelCase ,  lowerCamelCase      =   size, scale * w
										else:
													lowerCamelCase ,  lowerCamelCase      =   scale * h, size
										if max(_a						,					_a					) > self.max_size:
													lowerCamelCase      =   self.max_size * 1.0 / max(_a						,					_a					)
													lowerCamelCase      =   newh * scale
													lowerCamelCase      =   neww * scale
										lowerCamelCase      =   int(neww + 0.5					)
										lowerCamelCase      =   int(newh + 0.5					)

										if img.dtype == np.uinta:
													lowerCamelCase      =   Image.fromarray(_a					)
													lowerCamelCase      =   pil_image.resize((neww, newh)						,					PILImageResampling.BILINEAR					)
													lowerCamelCase      =   np.asarray(_a					)
										else:
													lowerCamelCase      =   img.permute(2						,					0						,					1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
													lowerCamelCase      =   nn.functional.interpolate(
													    _a						,					(newh, neww)						,					mode=self.interp_method						,					align_corners=_a					).squeeze(0					)
										img_augs.append(_a					)

							return img_augs






class        __magic_name__				:






				'''simple docstring'''





				def __init__(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,					cfg.INPUT.MAX_SIZE_TEST					)
							lowerCamelCase      =   cfg.INPUT.FORMAT
							lowerCamelCase      =   cfg.SIZE_DIVISIBILITY
							lowerCamelCase      =   cfg.PAD_VALUE
							lowerCamelCase      =   cfg.INPUT.MAX_SIZE_TEST
							lowerCamelCase      =   cfg.MODEL.DEVICE
							lowerCamelCase      =   torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)						,					1						,					1					)
							lowerCamelCase      =   torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)						,					1						,					1					)
							lowerCamelCase      =   lambda _a					: (x - self.pixel_mean) / self.pixel_std





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   tuple(max(_a					) for s in zip(*[img.shape for img in images]					)					)
							lowerCamelCase      =   [im.shape[-2:] for im in images]
							lowerCamelCase      =   [
							    nn.functional.pad(
							        _a						,					[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,					value=self.pad_value						,					)
							    for size, im in zip(_a						,					_a					)
							]

							return torch.stack(_a					), torch.tensor(_a					)





				def __call__(			self						,					_a						,					_a=False					):





							"""simple docstring"""
							with torch.no_grad():
										if not isinstance(_a						,					_a					):
													lowerCamelCase      =   [images]
										if single_image:
													assert len(_a					) == 1
										for i in range(len(_a					)					):
													if isinstance(images[i]						,					torch.Tensor					):
																images.insert(_a						,					images.pop(_a					).to(self.device					).float()					)
													elif not isinstance(images[i]						,					torch.Tensor					):
																images.insert(
																    _a						,					torch.as_tensor(img_tensorize(images.pop(_a					)						,					input_format=self.input_format					)					)
																    .to(self.device					)
																    .float()						,					)
            # resize smallest edge
										lowerCamelCase      =   torch.tensor([im.shape[:2] for im in images]					)
										lowerCamelCase      =   self.aug(_a					)
										# transpose images and convert to torch tensors
										# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
										# now normalize before pad to avoid useless arithmetic
										lowerCamelCase      =   [self.normalizer(_a					) for x in images]
										# now pad them to do the following operations
										lowerCamelCase ,  lowerCamelCase      =   self.pad(_a					)
										# Normalize

										if self.size_divisibility > 0:
													raise NotImplementedError()
										# pad
										lowerCamelCase      =   torch.true_divide(_a						,					_a					)
										if single_image:
													return images[0], sizes[0], scales_yx[0]
										else:
													return images, sizes, scales_yx




def        a__					(							snake_case__			,					snake_case__							)		->   Any:
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes



def        a__					(							snake_case__			,					snake_case__							)		->   Dict:
			assert torch.isfinite(snake_case__							).all(), "Box tensor contains infinite or NaN!"
			lowerCamelCase ,  lowerCamelCase      =   box_size
			tensor[:, 0].clamp_(min=0			,					max=snake_case__							)
			tensor[:, 1].clamp_(min=0			,					max=snake_case__							)
			tensor[:, 2].clamp_(min=0			,					max=snake_case__							)
			tensor[:, 3].clamp_(min=0			,					max=snake_case__							)









"""simple docstring"""




import random



def        a__					(							snake_case__			,					snake_case__			,					snake_case__ = False							)		->   dict:
			lowerCamelCase      =   {i: [] for i in range(snake_case__							)}

			# if probability is greater or equal than 1, then generate a complete graph
			if probability >= 1:
						return complete_graph(snake_case__							)
			# if probability is lower or equal than 0, then return a graph without edges
			if probability <= 0:
						return graph

			# for each couple of nodes, add an edge from u to v
			# if the number randomly generated is greater than probability probability
			for i in range(snake_case__							):
						for j in range(i + 1			,					snake_case__							):
									if random.random() < probability:
												graph[i].append(snake_case__							)
												if not directed:
															# if the graph is undirected, add an edge in from j to i, either
															graph[j].append(snake_case__							)
			return graph



def        a__					(							snake_case__							)		->   dict:
			return {
			    i: [j for j in range(snake_case__							) if i != j] for i in range(snake_case__							)
			}


if __name__ == "__main__":
					import doctest

					doctest.testmod()



